Update generated Python Op docs.

Change: 125729946

tensorflow/g3doc/api_docs/python/array_ops.md

@@ -727,7 +727,7 @@ tf.shape(tf.concat(1, [t3, t4])) ==> [2, 6]
 
 - - -
 
-### `tf.pack(values, name='pack')` {#pack}
+### `tf.pack(values, axis=0, name='pack')` {#pack}
 
 Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor.
 
@@ -743,6 +743,8 @@ This is the opposite of unpack.  The numpy equivalent is
 
 
 *  <b>`values`</b>: A list of `Tensor` objects with the same shape and type.
+*  <b>`axis`</b>: An `int`. The axis to pack along. Defaults to the first dimension.
+    Supports negative indexes.
 *  <b>`name`</b>: A name for this operation (optional).
 
 ##### Returns:
@@ -750,16 +752,21 @@ This is the opposite of unpack.  The numpy equivalent is
 
 *  <b>`output`</b>: A packed `Tensor` with the same type as `values`.
 
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `axis` is out of the range [-(R+1), R+1).
+
 
 - - -
 
-### `tf.unpack(value, num=None, name='unpack')` {#unpack}
+### `tf.unpack(value, num=None, axis=0, name='unpack')` {#unpack}
 
-Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
+Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
 
-Unpacks `num` tensors from `value` along the first dimension.
+Unpacks `num` tensors from `value` along the given dimension.
 If `num` is not specified (the default), it is inferred from `value`'s shape.
-If `value.shape[0]` is not known, `ValueError` is raised.
+If `value.shape[axis]` is not known, `ValueError` is raised.
 
 The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
 `output` has shape `value.shape[1:]`.
@@ -772,8 +779,10 @@ This is the opposite of pack.  The numpy equivalent is
 
 
 *  <b>`value`</b>: A rank `R > 0` `Tensor` to be unpacked.
-*  <b>`num`</b>: An `int`. The first dimension of value. Automatically inferred if
-    `None` (the default).
+*  <b>`num`</b>: An `int`. The length of the dimension `axis`. Automatically inferred
+    if `None` (the default).
+*  <b>`axis`</b>: An `int`. The axis to unpack along. Defaults to the first
+    dimension. Supports negative indexes.
 *  <b>`name`</b>: A name for the operation (optional).
 
 ##### Returns:
@@ -784,6 +793,7 @@ This is the opposite of pack.  The numpy equivalent is
 
 
 *  <b>`ValueError`</b>: If `num` is unspecified and cannot be inferred.
+*  <b>`ValueError`</b>: If `axis` is out of the range [-R, R).
 
 
 - - -

tensorflow/g3doc/api_docs/python/contrib.distributions.md

@@ -255,6 +255,13 @@ Generate `n` samples.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.strict` {#BaseDistribution.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.BaseDistribution.variance(name='variance')` {#BaseDistribution.variance}
@@ -441,6 +448,13 @@ Generate `n` samples.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.strict` {#ContinuousDistribution.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.ContinuousDistribution.variance(name='variance')` {#ContinuousDistribution.variance}
@@ -613,6 +627,13 @@ Generate `n` samples.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.strict` {#DiscreteDistribution.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.DiscreteDistribution.variance(name='variance')` {#DiscreteDistribution.variance}
@@ -862,6 +883,13 @@ Standard deviation of the distribution.
 *  <b>`std`</b>: `Tensor` of the same type and shape as `p`.
 
 
+- - -
+
+#### `tf.contrib.distributions.Bernoulli.strict` {#Bernoulli.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.variance(name='variance')` {#Bernoulli.variance}
@@ -895,19 +923,20 @@ Note, the following methods of the base class aren't implemented:
   * log_cdf
 - - -
 
-#### `tf.contrib.distributions.Categorical.__init__(logits, name='Categorical', dtype=tf.int32)` {#Categorical.__init__}
+#### `tf.contrib.distributions.Categorical.__init__(logits, dtype=tf.int32, strict=True, name='Categorical')` {#Categorical.__init__}
 
 Initialize Categorical distributions using class log-probabilities.
 
 ##### Args:
 
 
-*  <b>`logits`</b>: An N-D `Tensor` representing the log probabilities of a set of
-      Categorical distributions. The first N - 1 dimensions index into a
-      batch of independent distributions and the last dimension indexes
-      into the classes.
-*  <b>`name`</b>: A name for this distribution (optional).
+*  <b>`logits`</b>: An N-D `Tensor`, `N >= 1`, representing the log probabilities
+      of a set of Categorical distributions. The first `N - 1` dimensions
+      index into a batch of independent distributions and the last dimension
+      indexes into the classes.
 *  <b>`dtype`</b>: The type of the event samples (default: int32).
+*  <b>`strict`</b>: Unused in this distribution.
+*  <b>`name`</b>: A name for this distribution (optional).
 
 
 - - -
@@ -1074,6 +1103,13 @@ Sample `n` observations from the Categorical distribution.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Categorical.strict` {#Categorical.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Categorical.variance(name='variance')` {#Categorical.variance}
@@ -1090,16 +1126,26 @@ The Chi2 distribution with degrees of freedom df.
 
 The PDF of this distribution is:
 
-```pdf(x) = (x^(df/2 - 1)e^(-x/2))/(2^(k/2)Gamma(k/2)), x > 0```
+```pdf(x) = (x^(df/2 - 1)e^(-x/2))/(2^(df/2)Gamma(df/2)), x > 0```
 
 Note that the Chi2 distribution is a special case of the Gamma distribution,
 with Chi2(df) = Gamma(df/2, 1/2).
 - - -
 
-#### `tf.contrib.distributions.Chi2.__init__(df, name='Chi2')` {#Chi2.__init__}
+#### `tf.contrib.distributions.Chi2.__init__(df, strict=True, name='Chi2')` {#Chi2.__init__}
+
+Construct Chi2 distributions with parameter `df`.
 
+##### Args:
 
 
+*  <b>`df`</b>: `float` or `double` tensor, the degrees of freedom of the
+    distribution(s).  `df` must contain only positive values.
+*  <b>`strict`</b>: Whether to assert that `df > 0`, and that `x > 0` in the
+    methods `pdf(x)` and `log_pdf(x)`. If `strict` is False
+    and the inputs are invalid, correct behavior is not guaranteed.
+*  <b>`name`</b>: The name to prepend to all ops created by this distribution.
+
 
 - - -
 
@@ -1363,6 +1409,13 @@ See the doc for tf.random_gamma for further detail.
 Standard deviation of this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Chi2.strict` {#Chi2.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Chi2.variance(name='variance')` {#Chi2.variance}
@@ -1385,10 +1438,20 @@ Note that the Exponential distribution is a special case of the Gamma
 distribution, with Exponential(lam) = Gamma(1, lam).
 - - -
 
-#### `tf.contrib.distributions.Exponential.__init__(lam, name='Exponential')` {#Exponential.__init__}
+#### `tf.contrib.distributions.Exponential.__init__(lam, strict=True, name='Exponential')` {#Exponential.__init__}
 
+Construct Exponential distribution with parameter `lam`.
+
+##### Args:
 
 
+*  <b>`lam`</b>: `float` or `double` tensor, the rate of the distribution(s).
+    `lam` must contain only positive values.
+*  <b>`strict`</b>: Whether to assert that `lam > 0`, and that `x > 0` in the
+    methods `pdf(x)` and `log_pdf(x)`.  If `strict` is False
+    and the inputs are invalid, correct behavior is not guaranteed.
+*  <b>`name`</b>: The name to prepend to all ops created by this distribution.
+
 
 - - -
 
@@ -1649,6 +1712,13 @@ Sample `n` observations from the Exponential Distributions.
 Standard deviation of this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Exponential.strict` {#Exponential.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Exponential.variance(name='variance')` {#Exponential.variance}
@@ -1683,7 +1753,7 @@ dist2 = Gamma(alpha=[3.0, 4.0], beta=[2.0, 3.0])
 ```
 - - -
 
-#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, name='Gamma')` {#Gamma.__init__}
+#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, strict=True, name='Gamma')` {#Gamma.__init__}
 
 Construct Gamma distributions with parameters `alpha` and `beta`.
 
@@ -1699,6 +1769,9 @@ broadcasting (e.g. `alpha + beta` is a valid operation).
 *  <b>`beta`</b>: `float` or `double` tensor, the inverse scale params of the
     distribution(s).
     beta must contain only positive values.
+*  <b>`strict`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in the
+    methods `pdf(x)` and `log_pdf(x)`.  If `strict` is False
+    and the inputs are invalid, correct behavior is not guaranteed.
 *  <b>`name`</b>: The name to prepend to all ops created by this distribution.
 
 ##### Raises:
@@ -1962,6 +2035,13 @@ See the doc for tf.random_gamma for further detail.
 Standard deviation of this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Gamma.strict` {#Gamma.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Gamma.variance(name='variance')` {#Gamma.variance}
@@ -2018,7 +2098,7 @@ dist.pdf(3.0)
 ```
 - - -
 
-#### `tf.contrib.distributions.Normal.__init__(mu, sigma, name='Normal')` {#Normal.__init__}
+#### `tf.contrib.distributions.Normal.__init__(mu, sigma, strict=True, name='Normal')` {#Normal.__init__}
 
 Construct Normal distributions with mean and stddev `mu` and `sigma`.
 
@@ -2031,6 +2111,8 @@ broadcasting (e.g. `mu + sigma` is a valid operation).
 *  <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s).
 *  <b>`sigma`</b>: `float` or `double` tensor, the stddevs of the distribution(s).
     sigma must contain only positive values.
+*  <b>`strict`</b>: Whether to assert that `sigma > 0`. If `strict` is False,
+    correct output is not guaranteed when input is invalid.
 *  <b>`name`</b>: The name to give Ops created by the initializer.
 
 ##### Raises:
@@ -2272,6 +2354,13 @@ Distribution parameter for standard deviation.
 Standard deviation of this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Normal.strict` {#Normal.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Normal.variance(name='variance')` {#Normal.variance}
@@ -2331,7 +2420,7 @@ dist.pdf(3.0)
 ```
 - - -
 
-#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, name='StudentT')` {#StudentT.__init__}
+#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, strict=True, name='StudentT')` {#StudentT.__init__}
 
 Construct Student's t distributions.
 
@@ -2349,6 +2438,8 @@ broadcasting (e.g. `df + mu + sigma` is a valid operation).
 *  <b>`sigma`</b>: `float` or `double` tensor, the scaling factor for the
     distribution(s). `sigma` must contain only positive values.
     Note that `sigma` is not the standard deviation of this distribution.
+*  <b>`strict`</b>: Whether to assert that `df > 0, sigma > 0`. If `strict` is False
+    and inputs are invalid, correct behavior is not guaranteed.
 *  <b>`name`</b>: The name to give Ops created by the initializer.
 
 ##### Raises:
@@ -2543,6 +2634,13 @@ Scaling factors of these Student's t distribution(s).
 
 
 
+- - -
+
+#### `tf.contrib.distributions.StudentT.strict` {#StudentT.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.StudentT.variance(name='variance')` {#StudentT.variance}
@@ -2560,7 +2658,7 @@ Uniform distribution with `a` and `b` parameters.
 The PDF of this distribution is constant between [`a`, `b`], and 0 elsewhere.
 - - -
 
-#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, name='Uniform')` {#Uniform.__init__}
+#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, strict=True, name='Uniform')` {#Uniform.__init__}
 
 Construct Uniform distributions with `a` and `b`.
 
@@ -2590,12 +2688,14 @@ u1 = Uniform(3.0, [5.0, 6.0, 7.0])  # 3 distributions
 
 *  <b>`a`</b>: `float` or `double` tensor, the minimum endpoint.
 *  <b>`b`</b>: `float` or `double` tensor, the maximum endpoint. Must be > `a`.
+*  <b>`strict`</b>: Whether to assert that `a > b`. If `strict` is False and inputs
+    are invalid, correct behavior is not guaranteed.
 *  <b>`name`</b>: The name to prefix Ops created by this distribution class.
 
 ##### Raises:
 
 
-*  <b>`InvalidArgumentError`</b>: if `a >= b`.
+*  <b>`InvalidArgumentError`</b>: if `a >= b` and `strict=True`.
 
 
 - - -
@@ -2785,6 +2885,13 @@ Sample `n` observations from the Uniform Distributions.
 
 
 
+- - -
+
+#### `tf.contrib.distributions.Uniform.strict` {#Uniform.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Uniform.variance(name='variance')` {#Uniform.variance}
@@ -3092,7 +3199,7 @@ dist.pmf(counts)  # Shape [2]
 ```
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, name='DirichletMultinomial', allow_arbitrary_counts=False)` {#DirichletMultinomial.__init__}
+#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, allow_arbitrary_counts=False, strict=True, name='DirichletMultinomial')` {#DirichletMultinomial.__init__}
 
 Initialize a batch of DirichletMultinomial distributions.
 
@@ -3106,11 +3213,12 @@ Initialize a batch of DirichletMultinomial distributions.
 *  <b>`alpha`</b>: Positive `float` or `double` tensor with shape broadcastable to
     `[N1,..., Nm, k]` `m >= 0`.  Defines this as a batch of `N1 x ... x Nm`
      different `k` class Dirichlet multinomial distributions.
-*  <b>`name`</b>: The name to prefix Ops created by this distribution class.
 *  <b>`allow_arbitrary_counts`</b>: Boolean. This represents whether the pmf/cdf
     allows for the `counts` tensor to be non-integral values.
     The pmf/cdf are functions that can be evaluated at non-integral values,
     but are only a distribution over non-negative integers.
+*  <b>`strict`</b>: Not used (yet).
+*  <b>`name`</b>: The name to prefix Ops created by this distribution class.
 
 
 *  <b>`Examples`</b>: 
@@ -3339,6 +3447,13 @@ Generate `n` samples.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.strict` {#DirichletMultinomial.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.variance(name='variance')` {#DirichletMultinomial.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard0/tf.contrib.distributions.Bernoulli.md

@@ -232,6 +232,13 @@ Standard deviation of the distribution.
 *  <b>`std`</b>: `Tensor` of the same type and shape as `p`.
 
 
+- - -
+
+#### `tf.contrib.distributions.Bernoulli.strict` {#Bernoulli.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Bernoulli.variance(name='variance')` {#Bernoulli.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard1/tf.contrib.distributions.StudentT.md

@@ -45,7 +45,7 @@ dist.pdf(3.0)
 ```
 - - -
 
-#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, name='StudentT')` {#StudentT.__init__}
+#### `tf.contrib.distributions.StudentT.__init__(df, mu, sigma, strict=True, name='StudentT')` {#StudentT.__init__}
 
 Construct Student's t distributions.
 
@@ -63,6 +63,8 @@ broadcasting (e.g. `df + mu + sigma` is a valid operation).
 *  <b>`sigma`</b>: `float` or `double` tensor, the scaling factor for the
     distribution(s). `sigma` must contain only positive values.
     Note that `sigma` is not the standard deviation of this distribution.
+*  <b>`strict`</b>: Whether to assert that `df > 0, sigma > 0`. If `strict` is False
+    and inputs are invalid, correct behavior is not guaranteed.
 *  <b>`name`</b>: The name to give Ops created by the initializer.
 
 ##### Raises:
@@ -257,6 +259,13 @@ Scaling factors of these Student's t distribution(s).
 
 
 
+- - -
+
+#### `tf.contrib.distributions.StudentT.strict` {#StudentT.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.StudentT.variance(name='variance')` {#StudentT.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Categorical.md

@@ -9,19 +9,20 @@ Note, the following methods of the base class aren't implemented:
   * log_cdf
 - - -
 
-#### `tf.contrib.distributions.Categorical.__init__(logits, name='Categorical', dtype=tf.int32)` {#Categorical.__init__}
+#### `tf.contrib.distributions.Categorical.__init__(logits, dtype=tf.int32, strict=True, name='Categorical')` {#Categorical.__init__}
 
 Initialize Categorical distributions using class log-probabilities.
 
 ##### Args:
 
 
-*  <b>`logits`</b>: An N-D `Tensor` representing the log probabilities of a set of
-      Categorical distributions. The first N - 1 dimensions index into a
-      batch of independent distributions and the last dimension indexes
-      into the classes.
-*  <b>`name`</b>: A name for this distribution (optional).
+*  <b>`logits`</b>: An N-D `Tensor`, `N >= 1`, representing the log probabilities
+      of a set of Categorical distributions. The first `N - 1` dimensions
+      index into a batch of independent distributions and the last dimension
+      indexes into the classes.
 *  <b>`dtype`</b>: The type of the event samples (default: int32).
+*  <b>`strict`</b>: Unused in this distribution.
+*  <b>`name`</b>: A name for this distribution (optional).
 
 
 - - -
@@ -188,6 +189,13 @@ Sample `n` observations from the Categorical distribution.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Categorical.strict` {#Categorical.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Categorical.variance(name='variance')` {#Categorical.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Chi2.md

@@ -2,16 +2,26 @@ The Chi2 distribution with degrees of freedom df.
 
 The PDF of this distribution is:
 
-```pdf(x) = (x^(df/2 - 1)e^(-x/2))/(2^(k/2)Gamma(k/2)), x > 0```
+```pdf(x) = (x^(df/2 - 1)e^(-x/2))/(2^(df/2)Gamma(df/2)), x > 0```
 
 Note that the Chi2 distribution is a special case of the Gamma distribution,
 with Chi2(df) = Gamma(df/2, 1/2).
 - - -
 
-#### `tf.contrib.distributions.Chi2.__init__(df, name='Chi2')` {#Chi2.__init__}
+#### `tf.contrib.distributions.Chi2.__init__(df, strict=True, name='Chi2')` {#Chi2.__init__}
 
+Construct Chi2 distributions with parameter `df`.
+
+##### Args:
 
 
+*  <b>`df`</b>: `float` or `double` tensor, the degrees of freedom of the
+    distribution(s).  `df` must contain only positive values.
+*  <b>`strict`</b>: Whether to assert that `df > 0`, and that `x > 0` in the
+    methods `pdf(x)` and `log_pdf(x)`. If `strict` is False
+    and the inputs are invalid, correct behavior is not guaranteed.
+*  <b>`name`</b>: The name to prepend to all ops created by this distribution.
+
 
 - - -
 
@@ -275,6 +285,13 @@ See the doc for tf.random_gamma for further detail.
 Standard deviation of this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Chi2.strict` {#Chi2.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Chi2.variance(name='variance')` {#Chi2.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard2/tf.contrib.distributions.Uniform.md

@@ -3,7 +3,7 @@ Uniform distribution with `a` and `b` parameters.
 The PDF of this distribution is constant between [`a`, `b`], and 0 elsewhere.
 - - -
 
-#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, name='Uniform')` {#Uniform.__init__}
+#### `tf.contrib.distributions.Uniform.__init__(a=0.0, b=1.0, strict=True, name='Uniform')` {#Uniform.__init__}
 
 Construct Uniform distributions with `a` and `b`.
 
@@ -33,12 +33,14 @@ u1 = Uniform(3.0, [5.0, 6.0, 7.0])  # 3 distributions
 
 *  <b>`a`</b>: `float` or `double` tensor, the minimum endpoint.
 *  <b>`b`</b>: `float` or `double` tensor, the maximum endpoint. Must be > `a`.
+*  <b>`strict`</b>: Whether to assert that `a > b`. If `strict` is False and inputs
+    are invalid, correct behavior is not guaranteed.
 *  <b>`name`</b>: The name to prefix Ops created by this distribution class.
 
 ##### Raises:
 
 
-*  <b>`InvalidArgumentError`</b>: if `a >= b`.
+*  <b>`InvalidArgumentError`</b>: if `a >= b` and `strict=True`.
 
 
 - - -
@@ -228,6 +230,13 @@ Sample `n` observations from the Uniform Distributions.
 
 
 
+- - -
+
+#### `tf.contrib.distributions.Uniform.strict` {#Uniform.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Uniform.variance(name='variance')` {#Uniform.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.ContinuousDistribution.md

@@ -172,6 +172,13 @@ Generate `n` samples.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.ContinuousDistribution.strict` {#ContinuousDistribution.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.ContinuousDistribution.variance(name='variance')` {#ContinuousDistribution.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.DirichletMultinomial.md

@@ -67,7 +67,7 @@ dist.pmf(counts)  # Shape [2]
 ```
 - - -
 
-#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, name='DirichletMultinomial', allow_arbitrary_counts=False)` {#DirichletMultinomial.__init__}
+#### `tf.contrib.distributions.DirichletMultinomial.__init__(n, alpha, allow_arbitrary_counts=False, strict=True, name='DirichletMultinomial')` {#DirichletMultinomial.__init__}
 
 Initialize a batch of DirichletMultinomial distributions.
 
@@ -81,11 +81,12 @@ Initialize a batch of DirichletMultinomial distributions.
 *  <b>`alpha`</b>: Positive `float` or `double` tensor with shape broadcastable to
     `[N1,..., Nm, k]` `m >= 0`.  Defines this as a batch of `N1 x ... x Nm`
      different `k` class Dirichlet multinomial distributions.
-*  <b>`name`</b>: The name to prefix Ops created by this distribution class.
 *  <b>`allow_arbitrary_counts`</b>: Boolean. This represents whether the pmf/cdf
     allows for the `counts` tensor to be non-integral values.
     The pmf/cdf are functions that can be evaluated at non-integral values,
     but are only a distribution over non-negative integers.
+*  <b>`strict`</b>: Not used (yet).
+*  <b>`name`</b>: The name to prefix Ops created by this distribution class.
 
 
 *  <b>`Examples`</b>: 
@@ -314,6 +315,13 @@ Generate `n` samples.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.DirichletMultinomial.strict` {#DirichletMultinomial.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.DirichletMultinomial.variance(name='variance')` {#DirichletMultinomial.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Exponential.md

@@ -8,10 +8,20 @@ Note that the Exponential distribution is a special case of the Gamma
 distribution, with Exponential(lam) = Gamma(1, lam).
 - - -
 
-#### `tf.contrib.distributions.Exponential.__init__(lam, name='Exponential')` {#Exponential.__init__}
+#### `tf.contrib.distributions.Exponential.__init__(lam, strict=True, name='Exponential')` {#Exponential.__init__}
 
+Construct Exponential distribution with parameter `lam`.
+
+##### Args:
 
 
+*  <b>`lam`</b>: `float` or `double` tensor, the rate of the distribution(s).
+    `lam` must contain only positive values.
+*  <b>`strict`</b>: Whether to assert that `lam > 0`, and that `x > 0` in the
+    methods `pdf(x)` and `log_pdf(x)`.  If `strict` is False
+    and the inputs are invalid, correct behavior is not guaranteed.
+*  <b>`name`</b>: The name to prepend to all ops created by this distribution.
+
 
 - - -
 
@@ -272,6 +282,13 @@ Sample `n` observations from the Exponential Distributions.
 Standard deviation of this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Exponential.strict` {#Exponential.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Exponential.variance(name='variance')` {#Exponential.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.contrib.distributions.Gamma.md

@@ -20,7 +20,7 @@ dist2 = Gamma(alpha=[3.0, 4.0], beta=[2.0, 3.0])
 ```
 - - -
 
-#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, name='Gamma')` {#Gamma.__init__}
+#### `tf.contrib.distributions.Gamma.__init__(alpha, beta, strict=True, name='Gamma')` {#Gamma.__init__}
 
 Construct Gamma distributions with parameters `alpha` and `beta`.
 
@@ -36,6 +36,9 @@ broadcasting (e.g. `alpha + beta` is a valid operation).
 *  <b>`beta`</b>: `float` or `double` tensor, the inverse scale params of the
     distribution(s).
     beta must contain only positive values.
+*  <b>`strict`</b>: Whether to assert that `a > 0, b > 0`, and that `x > 0` in the
+    methods `pdf(x)` and `log_pdf(x)`.  If `strict` is False
+    and the inputs are invalid, correct behavior is not guaranteed.
 *  <b>`name`</b>: The name to prepend to all ops created by this distribution.
 
 ##### Raises:
@@ -299,6 +302,13 @@ See the doc for tf.random_gamma for further detail.
 Standard deviation of this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Gamma.strict` {#Gamma.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Gamma.variance(name='variance')` {#Gamma.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard3/tf.pack.md

-### `tf.pack(values, name='pack')` {#pack}
+### `tf.pack(values, axis=0, name='pack')` {#pack}
 
 Packs a list of rank-`R` tensors into one rank-`(R+1)` tensor.
 
@@ -14,6 +14,8 @@ This is the opposite of unpack.  The numpy equivalent is
 
 
 *  <b>`values`</b>: A list of `Tensor` objects with the same shape and type.
+*  <b>`axis`</b>: An `int`. The axis to pack along. Defaults to the first dimension.
+    Supports negative indexes.
 *  <b>`name`</b>: A name for this operation (optional).
 
 ##### Returns:
@@ -21,3 +23,8 @@ This is the opposite of unpack.  The numpy equivalent is
 
 *  <b>`output`</b>: A packed `Tensor` with the same type as `values`.
 
+##### Raises:
+
+
+*  <b>`ValueError`</b>: If `axis` is out of the range [-(R+1), R+1).
+

tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.unpack.md

-### `tf.unpack(value, num=None, name='unpack')` {#unpack}
+### `tf.unpack(value, num=None, axis=0, name='unpack')` {#unpack}
 
-Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
+Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
 
-Unpacks `num` tensors from `value` along the first dimension.
+Unpacks `num` tensors from `value` along the given dimension.
 If `num` is not specified (the default), it is inferred from `value`'s shape.
-If `value.shape[0]` is not known, `ValueError` is raised.
+If `value.shape[axis]` is not known, `ValueError` is raised.
 
 The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
 `output` has shape `value.shape[1:]`.
@@ -17,8 +17,10 @@ This is the opposite of pack.  The numpy equivalent is
 
 
 *  <b>`value`</b>: A rank `R > 0` `Tensor` to be unpacked.
-*  <b>`num`</b>: An `int`. The first dimension of value. Automatically inferred if
-    `None` (the default).
+*  <b>`num`</b>: An `int`. The length of the dimension `axis`. Automatically inferred
+    if `None` (the default).
+*  <b>`axis`</b>: An `int`. The axis to unpack along. Defaults to the first
+    dimension. Supports negative indexes.
 *  <b>`name`</b>: A name for the operation (optional).
 
 ##### Returns:
@@ -29,4 +31,5 @@ This is the opposite of pack.  The numpy equivalent is
 
 
 *  <b>`ValueError`</b>: If `num` is unspecified and cannot be inferred.
+*  <b>`ValueError`</b>: If `axis` is out of the range [-R, R).
 

tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.BaseDistribution.md

@@ -237,6 +237,13 @@ Generate `n` samples.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.BaseDistribution.strict` {#BaseDistribution.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.BaseDistribution.variance(name='variance')` {#BaseDistribution.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.DiscreteDistribution.md

@@ -158,6 +158,13 @@ Generate `n` samples.
 Standard deviation of the distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.DiscreteDistribution.strict` {#DiscreteDistribution.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.DiscreteDistribution.variance(name='variance')` {#DiscreteDistribution.variance}

tensorflow/g3doc/api_docs/python/functions_and_classes/shard7/tf.contrib.distributions.Normal.md

@@ -42,7 +42,7 @@ dist.pdf(3.0)
 ```
 - - -
 
-#### `tf.contrib.distributions.Normal.__init__(mu, sigma, name='Normal')` {#Normal.__init__}
+#### `tf.contrib.distributions.Normal.__init__(mu, sigma, strict=True, name='Normal')` {#Normal.__init__}
 
 Construct Normal distributions with mean and stddev `mu` and `sigma`.
 
@@ -55,6 +55,8 @@ broadcasting (e.g. `mu + sigma` is a valid operation).
 *  <b>`mu`</b>: `float` or `double` tensor, the means of the distribution(s).
 *  <b>`sigma`</b>: `float` or `double` tensor, the stddevs of the distribution(s).
     sigma must contain only positive values.
+*  <b>`strict`</b>: Whether to assert that `sigma > 0`. If `strict` is False,
+    correct output is not guaranteed when input is invalid.
 *  <b>`name`</b>: The name to give Ops created by the initializer.
 
 ##### Raises:
@@ -296,6 +298,13 @@ Distribution parameter for standard deviation.
 Standard deviation of this distribution.
 
 
+- - -
+
+#### `tf.contrib.distributions.Normal.strict` {#Normal.strict}
+
+Boolean describing behavior on invalid input.
+
+
 - - -
 
 #### `tf.contrib.distributions.Normal.variance(name='variance')` {#Normal.variance}